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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Using Risk Assessment Methodologies to Meet Management Objectives

Current decision making involves numerous possible combinations of technology elements, safety and health issues, operational aspects and process considerations to satisfy program goals. Identifying potential risk considerations as part of the management decision making process provides additional tools to make more informed management decision. Adapting and using risk assessment methodologies can generate new perspectives on various risk and safety concerns that are not immediately apparent. Safety and operational risks can be identified and final decisions can balance these considerations with cost and schedule risks. Additional assessments can also show likelihood of event occurrence and event consequence to provide a more informed basis for decision making, as well as cost effective mitigation strategies. Methodologies available to perform Risk Assessments range from qualitative identification of risk potential, to detailed assessments where quantitative probabilities are calculated. Methodology used should be based on factors that include: 1) type of industry and industry standards, 2) tasks, tools, and environment 3) type and availability of data and 4) industry views and requirements regarding risk & reliability. Risk Assessments are a tool for decision makers to understand potential consequences and be in a position to reduce, mitigate or eliminate costly mistakes or catastrophic failures.

DeMott, D. L.↗
NASA NTRS2022

On the Effects of Cloud Water Content on Passive Microwave Snowfall Retrievals

The Bayesian passive microwave retrievals of snowfall often rely on mathematical matching of the observed vectors of brightness temperature with an a priori database of precipitation profiles and their corresponding brightness temperatures. Mathematical proximity does not necessarily lead to consistent retrievals due to limited information content of passive microwave observations. This paper defines imposter (genuine) vectors of brightness temperature as those that are mathematically close but physically inconsistent (consistent) and characterizes them through the Silhouette Coefficient (SC) analysis. The Neyman–Pearson (NP) hypothesis testing is used to separate the imposter and genuine brightness temperatures based on their associated values of cloud ice (IWP) and liquid water path (LWP), given by coincidences of CloudSat Profiling Radar (CPR) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The study determines thresholds for IWP and LWP that allow optimal identification of imposter brightness temperatures of non-snowing and snowing clouds, which can mislead the passive microwave retrieval algorithms to falsely detect or miss the snowfall events. It is demonstrated that emission signal of supercooled liquid water in snowing clouds can lead to improved passive microwave retrieval of snowfall and conditioning the retrievals to the cloud IWP and LWP can result in marginal correction of the snowfall detection probability; however, reduce the probability of false alarm by 6%–8% over sea ice and open oceans.

Snowfall↗
DOE OSTI2025

A real-time search for Type Ia Supernovae with late-time interactions with circumstellar material in ZTF data

While it is generally accepted that Type Ia supernovae (SNe Ia) are the terminal explosions of white dwarfs (WDs), the nature of their progenitor systems and the mechanisms that lead up to these explosions remain widely debated. In rare cases, the SN ejecta interact with circumstellar material (CSM) that had previously been ejected from the progenitor system. The longer the delay between the creation of the CSM and the SN explosion, the greater the distance between the SN explosion site and the CSM and the later the onset of the interaction. The unknown distance between the CSM and SN explosion site makes it impossible to predict when the interaction will start. If the time between the SN explosion and the onset of the CSM interaction is of the order of several months to years, the SN has generally faded and it is no longer actively followed up on. This makes it even more difficult to detect the interaction while it is happening. In this work, we report on a real-time monitoring programme running between 13 November 2023 and 9 July 2024. It monitored 6914 SNe Ia for signs of late-time rebrightening using the Zwicky Transient Facility (ZTF). Flagged candidates were rapidly followed up on with photometry and spectroscopy to confirm the late-time excess and its position. We report the discovery of a ∼50 day rebrightening event in SN 2020qxz around 1200 rest-frame days after the peak of its light curve. SN 2020qxz exhibited signs of an early CSM interaction, but had faded from view over two years before its reappearance. Initial follow-up spectroscopy revealed the presence of four emission lines, while later follow-up spectroscopy showed that these had faded shortly after the end of the ZTF-detected rebrightening event. Our best match for these emission lines are H β (blueshifted by ∼5900 km s −1 ) and Ca II λ8542 , N I λ8567 , and K I λλ8763, 8767 (all blueshifted by 5100 km s −1 ; although we note that the line identifications are uncertain). This shows that catching and following up on late-time interactions as they occur can offer new clues on the nature of the progenitor systems that produce these SNe by putting constraints on the possible type of donor star. The only way to do this systematically is to use large sky surveys such as ZTF and the upcoming Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) to monitor a large sample of objects for the rare events that reappear long after the object has faded from view.

circumstellar matter↗
DOE OSTI2024

Luminosity determination using Z boson production at the CMS experiment

The measurement of Z boson production is presented as a method to determine the integrated luminosity of CMS data sets. The analysis uses proton–proton collision data, recorded by the CMS experiment at the CERN LHC in 2017 at a center-of-mass energy of 13 TeV. Events with Z bosons decaying into a pair of muons are selected. The total number of Z bosons produced in a fiducial volume is determined, together with the identification efficiencies and correlations from the same data set, in small intervals of 20 pb –1 of integrated luminosity, thus facilitating the efficiency and rate measurement as a function of time and instantaneous luminosity. Using the ratio of the efficiency-corrected numbers of Z bosons, the precisely measured integrated luminosity of one data set is used to determine the luminosity of another. For the first time, a full quantitative uncertainty analysis of the use of Z bosons for the integrated luminosity measurement is performed. The uncertainty in the extrapolation between two data sets, recorded in 2017 at low and high instantaneous luminosity, is less than 0.5%. We show that the Z boson rate measurement constitutes a precise method, complementary to traditional methods, with the potential to improve the measurement of the integrated luminosity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1977

Observations of ion cyclotron waves within the plasmasphere by Hawkeye 1

A survey of the plasma wave data from the Hawkeye 1 spacecraft has been performed in search of ion cyclotron waves associated with the scattering and loss of ring current ions within and near the plasmapause. During an 18-month period, encompassing about 270 orbits, a total of five events have been found with clearly detectable electric and magnetic fields at frequencies below the proton gyrofrequency. Comparisons of the electric and magnetic field amplitudes for these events provide strong evidence that these waves are ion cyclotron waves. All five events occurred during recovery phases of magnetic storms inside or very close to the plasmapause boundary. The results of this survey confirm and are consistent with the earlier identification of ion cyclotron waves by the Explorer 45 satellites. The Hawkeye 1 observations show that ion cyclotron waves of substantial amplitude occur at magnetic latitudes well away (about 28 deg) from the magnetic equator.

Kintner, P. M.↗
NASA NTRS1978

ISEE-C HKH high energy cosmic rays

The paper describes the ISEE-C multidetector cosmic ray telescope experiment. The HKH particle identifier sensor array is designed to identify the charge and mass of incident cosmic ray nuclei from H-1 to Ni-64 over the energy range of approximately 20 to 500 MeV/nucleon. Particle identification is based on the multiple energy loss technique. The scientific aspects of the experiment are briefly reviewed and consideration is given to the flight hardware, including sensors, event encoding, buffer memory, redundancy and commandability, and packaging.

Greiner, D. E.↗
NASA NTRS1987

The surface composition of Charon - Tentative identification of water ice

The Mar. 3, 1987, Charon occultation by Pluto was observed in the infrared at 1.5, 1.7, 2.0, and 2.35 micrometers. Subtraction of fluxes measured between second and third contacts from measurements made before and after the event has yielded individual spectral signatures for each body at these wavelengths. Charon's surface appears depleted in methane relative to Pluto. Constancy of flux at 2.0 micrometers throughout the event shows that Charon is effectively black at this wavelength, which is centered on a very strong water absorption band. Thus, the measurements suggest the existence of water ice on Pluto's moon.

Marcialis, Robert L.↗
NASA NTRS1993

Identification of two classes of gamma-ray bursts

We have studied the duration distribution of the gamma-ray bursts of the first BATSE catalog. We find a bimodality in the distribution, which separates GRBs into two classes: short events (less than 2 s) and longer ones (more than 2 s). Both sets are distributed isotropically and inhomogeneously in the sky. We find that their durations are anticorrelated with their spectral hardness ratios: short GRBs are predominantly harder, and longer ones tend to be softer. Our results provide a first GRB classification scheme based on a combination of the GRB temporal and spectral properties.

Kouveliotou, Chryssa↗
NASA NTRSDate not supplied

Hailstorm Analyses and Detection Derived from Current and Historical Satellite Data and Convective Environmental Parameters

We seek to demonstrate the extent to which hailstorms can be detected using a combination of geostationary (GEO) visible and infrared metrics of storm intensity and convective environmental parameters from reanalysis. Hailstorm identifications from low-Earth-orbiting (LEO) passive microwave sensors and maximum expected size of hail (MESH) from ground-based radar serve as a proxy for hail events. Data are analyzed for two warm seasons over the contiguous United States (CONUS). A neural network (NN) is trained to predict hailstorm detection dependent on optimal multi-variate weighting of observed and modeled input. The NN results are then applied to a 15-year Meteosat Second Generation climatology over South Africa to depict where hailstorms are most likely to occur. We also explore the impact of GEO imager resolution on our ability to discriminate hailstorms by matching GOES-13 (4 km) and GOES-16 (2 km) storm intensity metrics against hail characteristics using LEO, MESH, and spotter reports over CONUS during 2017, when both satellites were simultaneously imaging. Such analysis allows us to assess the feasibility of assembling a severe storm climate data record back to GOES-8, in the mid-1990’s. Finer spatial resolution of GOES-16 better resolves the updraft characteristics and intensities that are inherently linked to hail formation; however, GOES-13 can be normalized to achieve comparable detection capability. When intelligently combined with model-derived convective environmental parameters, GEO-derived storm intensity metrics enable high-spatial resolution hail risk assessment at hourly intervals throughout the diurnal cycle anywhere around the world and an improved understanding of hailstorms in the climate system.

Kyle F Itterly↗
NASA NTRS2021

Small-Body Proximity Operations & TAG: Navigation Experiences & Lessons Learned from the OSIRIS-REx Mission

On October 20th, 2020, the nearly two-year proximity operations campaign for the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx)mission at the near-Earth asteroid (101955) Bennu culminated in a successful Touch-and-Go (TAG) sample collection event. Navigation performance was a significant driver for flight activities at Bennu, which consisted of multiple phases geared towards characterizing the asteroid, selecting a sample site, and safely guiding the spacecraft to and from the surface in order to collect at least 60 g of pristine regolith. The entire operations team gained a tremendous amount of experience operating in the challenging small body environment and overcame many challenges. In this paper, we summarize navigation-focused experiences and lessons learned from OSIRIS-REx proximity operations at Bennu that are applicable to future missions to small asteroids, comets, and planetary moons. Areas of focus include staffing and organization, ground system infrastructure, mission phase planning, navigation operations, and spacecraft and payload considerations.

Kenneth M. Getzandanner↗
DOE OSTI2024

Criticality Analysis of FSV Spent Nuclear Fuel in the DOE Standard Canister

The U.S. Department of Energy (DOE) is responsible for managing over 300 types of spent nuclear fuel (SNF). To manage this large variety of fuel types, DOE plans to employ standardized canisters for the transportation, long-term storage, and eventual disposal of SNF. Idaho National Laboratory is currently supporting DOE’s SNF Packaging Demonstration Project, in which Fort Saint Vrain (FSV) fuel assemblies will be loaded into a DOE Standard Canister. This paper presents criticality calculations demonstrating that all four or five FSV fuel assemblies loaded into the DOE Standard Canister will remain subcritical in any expected normal or credible abnormal conditions. Previous criticality analyses were performed for one FSV fuel assembly and 12 Peach Bottom Core 2 fuel elements loaded into a DOE Standard Canister. This paper covers the criticality analysis performed for loading both four and five FSV fuel assemblies into a DOE Standard Canister. Various intact and degraded mode configurations were modeled in conducting the criticality calculations. This analysis encompassed three different configurations: (1) a single DOE Standard Canister loaded into a concrete storage overpack, (2) seven DOE Standard Canisters loaded into a concrete storage overpack, and (3) nine DOE Standard Canisters loaded into a concrete storage overpack. The overpack dimensions were varied for each of the three configurations, and transport, storage, and disposal scenarios were analyzed for each configuration. For transport scenarios, a pair of degradation cases was analyzed. In the first case, the fuel compacts became degraded and were removed from the fuel block, then deposited at the bottom of a horizontally placed canister, thereby simulating a drop event. The canister was considered to remain intact. In the second case, the spacing between horizontally placed canisters in a nine-canister overpack was reduced such that the canisters were piled on top of each other, simulating a drop event. For this case, no degradation of the canister internals or fuel was considered. For storage scenarios, the water moderator location in the system was varied to enable identification of the most reactive configurations. Dry and wet conditions were analyzed for the fuel materials, canister, and overpack. For disposal scenarios, two degradation cases were analyzed. In the first, the stainless-steel internals of the canister degraded to either hematite or goethite under both dry and wet conditions. In the second case, degraded FSV fuel formed a uranium-water slurry that filled the coolant/void holes. None of the cases presented exceeded the application specific upper subcritical limit.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Calibration of the DUNE Far Detector Using Cosmic-ray Muon Events

The Deep Underground Neutrino Experiment (DUNE) aims to set new limits on parameters associated with neutrino oscillations, neutrino astrophysics, and beyond the Standard Model (SM) searches such as nucleon decay. DUNE will quantify the magnitude of CP violation in the lepton sector, and determine the neutrino mass ordering. These benefit highly from the large target mass and excellent imaging, tracking, and particle identification capabilities of Liquid Argon Time Projection Chambers (LArTPCs). Detector calibration is essential to make precise physics measurements. For instance, accurate energy reconstruction is necessary for measuring many of the aforementioned quantities with the precision required for discovering new physics and fully exploiting the capabilities of the detector. Cosmic muons are a freely available natural source of calorimetric data and can be used for calibrating various detector parameters. This thesis provides an analysis of simulated cosmic-ray muon events generated with the Muon Simulation Underground (MUSUN) generator in the DUNE horizontal drift (HD) far detector (FD). The study focuses on analysing the energy and angular distribution of various classes of muon events, as well as characterising the different particles produced by cosmic muon interactions. The analysis of π0 → 2γ events within the cosmic-ray muon sample is presented in this thesis with a detailed study of reconstructing electromagnetic showers. The π0 mass is reconstructed within the DUNE FD, yielding a value of (136 ± 7) MeV/c2. Additionally, the thesis introduces methods for dE/dx calibration using simulated and reconstructed muon tracks. A calibration constant Ccal = (5.469 ± 0.003) × 10−3 ADC × tick/e is obtained through a model-dependent calibration process, where 1 tick corresponds to 500 ns of sampling time of an ADC. Furthermore, a calibration technique is presented, demonstrating precise translation from dQ/dx to dE/dx. This calibration method is applied to stopping muons, charged pions, and protons in the DUNE FD, addressing the measurement of energy loss in the detector volume. These are important calibrations of the DUNE FD and will contribute to achieving the exciting physics goals of the experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Anomaly Detection and Identification Using a Leave-One-Variable-Out Method

At nuclear power plants (NPPs), anomaly detection and identification (i.e., determining the causes of anomalies) are important tasks for ensuring the safe and efficient operation of NPPs. These tasks are currently labor-intensive and costly, and are made more difficult by the size and complexity of NPP systems. An alternative approach to conducting these tasks is to automate them, such as via the reconstruction-based contribution method, which is a well-researched unsupervised machine learning method that uses a data-driven model of anomaly-free behavior to detect events and then identify each variable’s contributions to those events. The present effort developed a novel contribution approach that utilized a leave-one-variable-out (LOVO) model, with which each variable is predicted using all the other variables. The novelty lay in transforming this model into a reconstruction model and modifying the identification algorithm to work with the new reconstruction model. To evaluate this method in a controlled environment, a synthetic dataset based on spring-mass-damper (SMD) systems (commonly found in mechanical engineering references) was used, with known anomalies introduced into the system. The proposed method successfully detected the anomalies and afforded insights into their causes, thus enabling the appropriate identifications to be made.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2001

Hybrid Modeling for Scenario-Based Evaluation of Failure Effects in Advanced Hardware-Software Designs

This paper describes an incremental scenario-based simulation approach to evaluation of intelligent software for control and management of hardware systems. A hybrid continuous/discrete event simulation of the hardware dynamically interacts with the intelligent software in operations scenarios. Embedded anomalous conditions and failures in simulated hardware can lead to emergent software behavior and identification of missing or faulty software or hardware requirements. An approach is described for extending simulation-based automated incremental failure modes and effects analysis, to support concurrent evaluation of intelligent software and the hardware controlled by the software

Malin, Jane T.↗
NASA NTRS2024

Climate Extremes and Risks: Links Between Climate Science and Decision-Making

The World Climate Research Programme (WCRP) envisions a future where actionable climate information is universally accessible, supporting decision makers in preparing for and responding to climate change. In this perspective, we advocate for enhancing links between climate science and decision-making through a better and more decision-relevant understanding of climate impacts. The proposed framework comprises three pillars: climate science, impact science, and decision-making, focusing on generating seamless climate information from sub-seasonal, seasonal, decadal to century timescales informed by observed climate events and their impacts. The link between climate science and decision-making has strengthened in recent years, partly owing to undeniable impacts arising from disastrous weather extremes. Enhancing decision-relevant understanding involves utilizing lessons from past extreme events and implementing impact-based early warning systems to improve resilience. Integrated risk assessment and management require a comprehensive approach that encompasses good knowledge about possible impacts, hazard identification, monitoring, and communication of risks while acknowledging uncertainties inherent in climate predictions and projections, but not letting the uncertainty lead to decision paralysis. The importance of data accessibility, especially in the Global South, underscores the need for better coordination and resource allocation. Strategic frameworks should aim to enhance impact-related and open-access climate services around the world. Continuous improvements in predictive modeling and observational data are critical, as is ensuring that climate science remains relevant to decision makers locally and globally. Ultimately, fostering stronger collaborations and dedicated investments to process and tailor climate data will enhance societal preparedness, enabling communities to navigate the complexities of a changing climate effectively.

climate extremes↗
NASA NTRSDate not supplied

Development of the Suited Injury Modes and Effects Analysis for Identification of Top Injury Risks in Lunar Missions and Training

A new Exploration Extravehicular Activity Services (xEVAS) suit is being designed to replace the current Extravehicular Mobility Unit (EMU) for the National Aeronautics and Space Administration’s (NASA’s) Artemis program to return astronauts to the lunar surface. This new suit will allow for increased range of motion compared to the current EMU and Apollo era suits and additional features will enhance the health and safety of exploration. With the design of lunar missions and the xEVAS suit progressing, it is important to consider possible injuries and injury mechanisms that could occur in the suit. To address these concerns, the suited Injury Modes and Effects Analysis (IMEA) was developed to outline suited injury scenarios and rank them based on risk score. The IMEA documents possible scenarios and underlying mechanisms of injury. History has shown that more suit injuries occur during training than in flight; therefore, currently planned training events to prepare for lunar missions and tasks during lunar surface EVAs were considered. Each scenario is ranked with likelihood and consequence scorings based on our current understanding of suit and application of Artemis design reference missions. The scoring allowed identification of the high-risk cases that will drive further work in suited injury. Mechanisms of injury, injury outcomes, and mitigation strategies are evaluated within each scenario. The Suited Injury Summit was held on January 5, 2022, to vet the IMEA with external experts. This was an all-day virtual meeting with the suited injury team; ergonomists; suit engineers; safety engineers; the flight operations directorate; flight doctors; astronauts; astronaut strength, conditioning, and rehabilitation specialists (ASCRS); and external subject matter experts (SMEs). External SMEs consisted of surgeons with varying specialties. The intent of this meeting was to walk through the top injury risks identified in the analysis, identify any gaps that were not captured, and discuss mitigations. With participation from all groups, countless lessons-learned came from the Summit meeting. Using these, the top 10 risks have been identified: neutral buoyancy laboratory training, hand/glove injuries, poor suit fit, field training, specific EVA tasks/design of task, boots/ankle injuries, falls from heights, background radiation, repetitive contact, and ambulation/longdistance ambulation. Mitigation steps have also been determined for each of the top risks. The IMEA and documentation of top risks is a living document. Yearly meetings are planned to update the analysis and reevaluate top risks and mitigations. The IMEA is being used to drive work in suited injury, and this work will continue to evolve with IMEA and lunar mission updates.

Teresa Reiber↗